Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery

@article{Heinz2001FullyCL,
  title={Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery},
  author={Daniel C. Heinz and C. Chang},
  journal={IEEE Trans. Geoscience and Remote Sensing},
  year={2001},
  volume={39},
  pages={529-545}
}
Linear spectral mixture analysis (LSMA) is a widely used technique in remote sensing to estimate abundance fractions of materials present in an image pixel. In order for an LSMA-based estimator to produce accurate amounts of material abundance, it generally requires two constraints imposed on the linear mixture model used in LSMA, which are the abundance sum-to-one constraint and the abundance nonnegativity constraint. The first constraint requires the sum of the abundance fractions of… CONTINUE READING

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